BIOINFORMATICS: A CONCEPT-BASED INTRODUCTION
BIOINFORMATICS: A CONCEPT-BASED INTRODUCTION
Venkatarajan Subramanian Mathura, Ph.D Bioinformatics Scientist, Roskamp Institute Sarasota, Florida, USA and Pandjassarame Kangueane, Ph.D Managing Director, Biomed-Informatics Pondicherry, India
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Venkatarajan S. Mathura Roskamp Institute 2040 Whitfield Avenue Sarasota, FL 34243
Pandjassarame Kangueane Biomed-Informatics 17A, Main Road Irulan Chandai Annex Pondicherry 607 402, India
ISBN: 978-0-387-84869-3 e-ISBN: 978-0-387-84870-9 DOI: 10.1007/ 978-0-387-84870-9 Library of Congress Control Number: 2008932359 © Springer Science+Business Media, LLC 2009 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of going to press, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Cover illustration: Ribbon diagram of the Cytoplasmic Domain of GProtein-Gated Inward Rectifier Potassium Channel Ki r3.2 (PDB:2e4f ) using rendering program pyMOL. Printed on acid-free paper springer.com
This book is dedicated to our families
Preface
Scientific disciplines evolve and mature into different areas of specialization to accommodate new knowledge and methods that are being developed by the research community. The last decade has seen a dramatic change in most fields and the information technology has revolutionized several fields. Bioinformatics is the perfect marriage between computer science and advanced biology. Complex biological processes, macromolecular components and their functional interplay define the basis of living cells. Biological experiments that aim to reveal the complexity of cellular systems and biomolecular functions produce huge volumes of data or information that needs to be efficiently handled for tangible results. Exponential increase in genome sequences, protein sequences, protein interactions and biological networks/pathways information has created a demand for efficient information handling. This led to the birth of the field of Bioinformatics that aims to handle biological information using computational methods and algorithms. Bioinformatics is evolving into a mature field with an everincreasing participation from the scientific community. The past five years have seen a rapid increase in the number of scientific journals in this field. It is impossible to include all the topics of Bioinformatics in a book and still cater to the needs of newcomers attracted to this field. This is an introductory book that provides a balance between computational methods and biological information. Instead of delving in depth, for each topic we provide a broad but necessary content that will benefit readers with different levels of expertise. Venkatarajan S. Mathura and Pandjassarame Kangueane Editors
Acknowledgments
We thank Mr. Joseph Burns and Ms. Marcia Kidston of Springer for their help and encouragement. We also thank Ms. Archchana Alagiriswammy for the help with formatting the book. We extend our sincere thanks to all the chapter authors for their contributions and dedication to complete this book. Venkatarajan S. Mathura and Pandjassarame Kangueane Editors
Contents
1 Introduction to Biological Systems........................................................ 1 Claude-Henry Volmar, Nikunj Patel, Amita N. Quadros, Daniel Paris, Venkatarajan S. Mathura and Michael Mullan 1. Molecules of Life.................................................................................. 1 2. Nucleic Acids: DNA Versus RNA ....................................................... 2 3. Understanding Proteins: Sequence–Structure–Function....................... 4 4. Biological Systems, Signals, and Pathways.......................................... 5 5. Technological Advances and Their Benefits to Biology ...................... 7 6. The Role of Bioinformatics in Big Picture ........................................... 8 7. Exercises ............................................................................................... 9 References............................................................................................... 10 2 Computer Programming Fundamentals and Concepts .................... 13 Deepak N. Kolippakkam, Pankaj Gupta and Venkatarajan S. Mathura 1. Purpose ............................................................................................... 13 2. Learning Objective ............................................................................. 13 3. Perl Programming ............................................................................... 14 3.1 Variables ....................................................................................... 14 3.2 Operators....................................................................................... 15 3.3 Control Structures ......................................................................... 16 3.4 Regular Expressions ..................................................................... 17 3.5 File Handling ................................................................................ 18 3.6 Subroutines and Functions............................................................ 18
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Contents 4. PHP Programming .............................................................................. 19 4.1 Language Syntax and Data Types................................................. 19 4.2 Creating Web Interfaces ............................................................... 22 5. Basic RDBMS and SQL ..................................................................... 24 5.1 Data Definition Language (DDL)................................................. 24 5.2 Data Manipulation Language (DML) ........................................... 25 5.3 Data Control Language (DCL) ..................................................... 26 6. Web-Pointers ...................................................................................... 26
3 Introduction to Algorithms .................................................................. 27 Senthilkumar Radhakrishnan, Deepak Kolippakkam and Venkatarajan S. Mathura 1. Introduction......................................................................................... 27 1.1 Classification ................................................................................ 27 1.2 Hypothesis Testing ....................................................................... 28 1.3 Decision Tree................................................................................ 28 1.4 Clustering...................................................................................... 29 1.5 Principal Component Analysis ..................................................... 29 1.6 Multidimensional Scaling ............................................................. 29 1.7 Regression Analysis...................................................................... 29 1.8 Linear Discriminant Analysis ....................................................... 30 1.9 Fuzzy Logic .................................................................................. 30 1.10 Pattern Recognition..................................................................... 31 1.11 Bayesian Statistics ...................................................................... 31 1.12 Neural Networks ......................................................................... 32 1.13 Hidden Markov Model................................................................ 32 1.14 Support Vector Machines ........................................................... 33 2. Exercises ............................................................................................. 33 3. Useful Web-Pointers........................................................................... 34 References............................................................................................... 35 4 Biological Sequence Databases ............................................................ 39 Meena Sakharkar, Pandjassarame Kangueane and Venkatarajan S. Mathura 1. Purpose ............................................................................................... 39 2. Learning Objective ............................................................................. 39 3. Introduction......................................................................................... 39 3.1 Genomic Sequence Databases – GenBank, EMBL, DDBJ .......... 41 3.2 Protein Sequence Databases ......................................................... 42 3.3 Secondary Databases on Molecular Evolution ............................. 44 References............................................................................................... 46
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5 Biological Sequence Search and Analysis ........................................... 47 Venkatarajan S. Mathura 1. Purpose ............................................................................................... 47 2. Learning Objectives............................................................................ 47 3. Introduction......................................................................................... 48 3.1 Similarity Matrices and Alignment............................................... 48 3.2 Sequence Search and Pair-Wise Alignment ................................. 50 3.3 Global Alignment Using Needleman-Wunsch Algorithm............ 51 3.4 Sequence Search Tools ................................................................. 53 3.5 Pair-Wise and Multiple-Sequence Alignment Tools .................... 55 3.6 Sequence Motifs ........................................................................... 57 References............................................................................................... 61 6 Protein Structure Prediction................................................................ 63 Hongyi Zhou, Yaoqi Zhou and Venkatarajan S. Mathura 1. Introduction......................................................................................... 63 2. Secondary Structure Prediction .......................................................... 65 3. Comparative Modeling ....................................................................... 66 3.1 Steps Involved in Comparative Modeling .................................... 67 3.2 Homologous Sequence Search Using Sequence Comparison Tools......................................................................... 67 3.3 Identifying Remote Templates Using Fold-Recognition Methods ........................................................................................ 68 3.4 Selection of the Alignment ........................................................... 69 3.5 Construction of 3D Models Using Modeling Programs ............... 69 3.6 Protein Modeling Package – MPACK.......................................... 70 3.7 SP3 – A Web-Based Structure-Prediction Tool Using Known Protein Structures as Templates ....................................... 70 3.8 Modeling Servers.......................................................................... 73 3.9 Critical Assessment of Structure Prediction ................................. 74 3.10 Objective Testing of Modeling Tools in CASP .......................... 74 References............................................................................................... 75 7 Protein-Protein Interaction and Macromolecular Visualization...... 79 Arun Ramani, Venkatarajan S. Mathura, Cui Zhanhua and Pandjassarame Kangueane 1. Introduction......................................................................................... 79 2. Experimental Methods........................................................................ 80 2.1 Yeast Two-Hybrid ........................................................................ 80 2.2 Affinity Tagging ........................................................................... 81 2.3 Computational Methods................................................................ 82 2.4 Co-evolution ................................................................................. 83
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Contents 2.5 Structure Based Methods .............................................................. 83 3. Protein Structure Visualization........................................................... 91 4. Databases ............................................................................................ 91 References............................................................................................... 93
8 Genes, Genomics, Microarray Methods and Analysis ...................... 97 Ghania Ait-Ghezala and Venkatarajan S. Mathura 1. Introduction......................................................................................... 97 2. Gene Identification and Characterization ........................................... 98 2.1 Identifying Human Genes and Cloning ........................................ 98 3. Microarray Experiments ................................................................... 102 3.1 Microarray Databases ................................................................. 104 3.2 Gene Annotations, Ontology, and Pathway Databases............... 104 References............................................................................................. 105 9 Introduction to Proteomics ................................................................ 107 Fai Poon and Venkatarajan S. Mathura 1. Introduction....................................................................................... 107 2. Sample Preparation ........................................................................... 108 3. Two-Dimensional (2D) Gel Electrophoresis .................................... 108 3.1 Image Analysis and Statistical Analysis ..................................... 109 3.2 In-Gel Digestion and Mass Spectrometry................................... 109 4. Mass Spectrometry ........................................................................... 109 4.1 Mass Spectrometry in Proteomics .............................................. 110 5. Bioinformatics Applications for Identification................................. 111 6. Conclusion ........................................................................................ 113 References............................................................................................. 113 10 Biomedical Literature Mining ........................................................... 115 Chaolin Zhang and Michael Q. Zhang 1. Introduction....................................................................................... 115 2. Literature Sources for Mining........................................................... 117 3. Recognition of Biological Terms...................................................... 118 3.1 Gene/Protein Name Recognition ................................................ 119 3.2 Removing Gene/Protein Name Ambiguities .............................. 120 3.3 Collecting Other Keywords ........................................................ 120 4. Mining Biological Relationships ...................................................... 121 4.1 Detecting Gene Interactions by Co-occurrence .......................... 121 4.2 Inferring Implicit Relationships.................................................. 122 4.3 Identifying Sub-networks of Communities................................. 123 4.4 Evaluating Functional Coherence of Gene Group ...................... 124 5. Acknowledgments ............................................................................ 124 References............................................................................................. 125
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11 Computational Immunology: HLA-Peptide Binding Prediction.... 129 Pandjassarame Kangueane, Bing Zhao and Meena K. Sakharkar 1. Background....................................................................................... 129 2. HLA Molecules ................................................................................ 131 3. HLA Binding Peptide Based Methods.............................................. 132 3.1 Sequence Based Prediction Models ............................................ 133 3.2 Molecular Structure Based Predictions....................................... 143 4. Conclusion ........................................................................................ 150 References............................................................................................. 151 12 Bioinformatics Application: Eukaryotic Gene Count and Evolution ........................................................................... 155 Meena K. Sakharkar and Pandjassarame Kangueane 1. Introduction....................................................................................... 155 2. Methodology..................................................................................... 156 2.1 Identification of SEG .................................................................. 156 2.2 Identification of MEG................................................................. 156 2.3 Pseudogenes................................................................................ 157 2.4 Caveats........................................................................................ 157 2.5 Total Genes ................................................................................. 158 3. Results and Discussion ..................................................................... 158 3.1 Utility of SEG and MEG Sequences to the Study of Evolution.... 158 3.2 Selection of SEG and MEG in Different Eukaryotic Genomes .... 158 3.3 Mechanism of SEG Origin ......................................................... 160 4. Conclusion ........................................................................................ 161 References............................................................................................. 162 13 Bioinformatics Application: Predicting Protein Subcellular Localization by Applying Machine Learning ................................... 163 Pingzhao Hu, Clement Chung, Hui Jiang and Andrew Emili 1. Introduction....................................................................................... 163 2. Methods ............................................................................................ 165 2.1 Data Sets and Preprocessing ....................................................... 165 2.2 Learning Algorithm .................................................................... 166 2.3 Evaluating Performance of the Learning Algorithm................... 167 2.4 Strategy for Multi-class/Multi-label Classification..................... 167 2.5 Optimal Sampling Methods for Imbalanced Data Sets............... 168 2.6 Algorithm of Asymmetric Bagging Strategy .............................. 169 3. Results............................................................................................... 170 4. Discussion......................................................................................... 172 References............................................................................................. 172
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14 Bioinformatics Analysis: Gene Fusion .............................................. 175 Meena Kishore Sakharkar, Yiting Yu and Pandjassarame Kangueane 1. Introduction....................................................................................... 175 2. Identification of Fusion Proteins....................................................... 176 2.1 Human Fusion Proteins Mimicking Bacterial Operons .............. 177 2.2 Human Fusion Proteins Simulating Bacterial Subunit Interfaces..................................................................................... 177 2.3 Fusion Proteins Exhibiting Multiple Functions .......................... 177 2.4 Fusion Proteins Showing Alternative Splicing ........................... 178 3. Remarks on Fusion Proteins ............................................................. 178 References............................................................................................. 180 Index .......................................................................................................... 183
Contributing Authors
Ghania Ait-Ghezala
Roskamp Institute, Sarasota, Florida
Clement Chung
University of Toronto, Toronto, Canada
Andrew Emili
University of Toronto, Toronto, Canada
Pankaj Gupta
Roskamp Institute, Sarasota, Florida
Pingzhao Hu
University of Toronto, Toronto, Canada
Hui Jiang
York University, Toronto, Canada
Deepak N. Kolippakkam
Harvard University, Boston, Massachusets
Venkatarajan S. Mathura
Roskamp Institute, Sarasota, Florida
Michael Mullan
Roskamp Institute, Sarasota, Florida
Pandjassarame Kangueane
Biomedical Informatics, Pondicherry, India
Daniel Paris
Roskamp Institute, Sarasota, Florida
Nikunj Patel
Roskamp Institute, Sarasota, Florida
Fai Poon
Roskamp Institute, Sarasota, Florida
Amita N. Quadros
Roskamp Institute, Sarasota, Florida
Senthilkumar Radhakrishnan
Cal Tech, Pasadena, California
Arun Ramani
University of Texas, Austin, Texas
Meena Sakharkar
Nanyang Technological University, Singapore
Claude-Henry Volmar
Roskamp Institute, Sarasota, Florida
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Contributing Authors
Yiting Yu
Nanyang Technological University, Singapore
Michael Q. Zhang
CSHL, Cold Spring Harbor, New York
Chaolin Zhang
SUNY, Stony Brook, New York
Cui Zhanhua
Nanyang Technological University, Singapore
Bing Zhao
Nanyang Technological University, Singapore
Hongyi Zhou
State University of New York, Buffalo, New York
Yaoqi Zhou
State University of New York, Buffalo, New York
Chapter 1 Introduction to Biological Systems Claude-Henry Volmar, Nikunj Patel, Amita N. Quadros, Daniel Paris, Venkatarajan S. Mathura, and Michael Mullan Roskamp Institute, 2040 Whitfield Avenue, Sarasota, Florida 34243, USA
Abstract:
Living organisms are composed of macromolecules like DNA, RNA, proteins, and carbohydrates that dictate various processes. This chapter provides a glimpse of biological macromolecules and their interplay resulting in biological process or pathways.
Key words:
Proteins, Amino acids, DNA, RNA, Cell signaling, Biological systems
1. Molecules of Life Biochemical molecules such as deoxy ribo nucleic acid (DNA), ribo nucleic acid (RNA), proteins, carbohydrates, and lipids are fundamental for cellular organization and their complex interplay with each other dictates various aspects of living things. They enable a systematic execution of numerous biological processes in a defined manner to maintain life at the cellular level (Kitano, 2002; Noble, 2002). The genetic materials (DNA and RNA) are tightly regulated in organisms. At any given moment, organisms have to deal with different pressures (internal or external) by controlling various biochemical molecules thus maintaining a balance or in other terms, homeostasis. The proper function of biochemical molecules is crucial to the survival of any given organism. Since mutations and other modifications caused by selective pressures are sometimes irreparable, organisms often have to adapt in order to survive and pass on their genes to the next generation. Organisms, therefore, evolve. The mechanisms involved in such a difficult task as maintaining the basic life of an organism are very complex. Regulation at the molecular level is essential for the maintenance
V.S. Mathura, P. Kangueane, Bioinformatics: A Concept-Based Introduction, DOI 10.1007/978-0-387-84870-9_1, © Springer Science+Business Media, LLC 2009
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of life at the cellular level. Problems at the molecular level often result in physiological alterations that in turn affect homeostasis of the whole organism. The life of an organism is mapped in its genome, a long sequence of nucleic acids that consists of the entire set of chromosomes of the organism. Genes are a stretch of nucleic acids, which represent a functional aspect of the genome. Each gene codes for a limited set of proteins. The same genes may be found in very distant animals such as a cow and a jellyfish, but their regulation (control of the activity of those genes) may be different and appears to be of utmost importance. Cellular processes such as apoptosis or programmed cell death are encoded within the genome of individual organisms in a complex manner. With the recent sequencing of the human genome, mankind has for the first time the opportunity to attempt to understand the involvement of the genes in sequence of events involved in the development of an organism (ontogenesis) as well as in the etiology of various diseases. RNA is the product of the transcription of DNA and is then translated into polypeptide, which folds into a functional form called protein. Any mutation in DNA, if not repaired by the various polymerases, may result in the transcription of faulty RNA resulting in a wrong protein being translated lacking its original activity. This may cause major problems such as protein aggregation and misfolded proteins, which are not degradable and result in fatal diseases. Naturally occurring single nucleotide polymorphism (SNP) among human population may influence gene function and expression in individuals. Functional variants or genetic changes like SNPs that alter amino acids in proteins, gene expression, and gene splicing are of great interest. The first step of regulation is trying to fix problems at the DNA levels. The next step is to mend at the RNA level through gene splicing and then at the protein level via proteosome/ubiquitin pathways. In eukaryotes, higher-level organism, DNA is transcribed to RNA (Pre-mRNA) that consists of introns and exons. The exons possess the codes that will be translated into proteins whereas the introns are eventually cut out through gene splicing. The resulting RNA is referred to as messenger RNA (mRNA). This messenger RNA may or may not get translated into peptide that folds into a functional protein.
2. Nucleic Acids: DNA Versus RNA Nucleic acids are made of long chains of nucletotides that consist of nitrogenous base, a sugar moiety, and phosphodieseter connections. Deoxyribonucleic acid (DNA) is basically a sequence of nucleic acids that
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exists as a double helix. It consists of the nitrogenous bases adenine (A), thymine (T), cytosine (C), and guanine (G) (Watson and Crick, 1953). Adenine and guanine are purines whereas Thymine and Cytosine are pyrimidines (Figure 1.1). In the DNA double helix, purines always pair with pyrimidines by weak hydrogen bonds. This pairing is based on the Watson and Crick complementation of A-T and G-C. This pairing results in a double helix with a constant diameter of 20 Angstrom (Å), with a complete helical turn every 34 Å, and consists of 10 bases per turn. Each branch of the DNA double helix consists of a stretch of nucleotides (nitrogenous bases attached to a sugar phosphate backbone). The two branches are then held together via hydrogen bonds between purines and pyrimidines that are on opposite sides. This knowledge was crucial in understanding the process of heredity. DNA has a semi-conservative replication (Meselson and Stahl, 1958). The double helix opens up (in a fork-like fashion) and each strand serves as a parental template for replication of the DNA. The replication occurs from 5’ to 3’ by DNA polymerase. Each daughter strand ends up being the
Figure 1.1 Nitrogenous bases and nucleotides in DNA.
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complement of a parental strand. Subsequently, each replicated DNA fragment has one parental strand and one daughter strand, hence the term semi-conservative. The genetic make-up of an individual is termed genotype. Most of the DNA sequences among individuals are conserved but genetic variation in 0.1% of DNA influences disease risk, metabolic activity, and drug response. It is important to map occurrence of variation in the human genome, which can help to identify allelic polymorphisms that result in disease. Computational techniques that can rapidly compare entire genome and genes will help to identify polymorphism among population. Comparative genomics is a field in which DNA sequences across several genomes are compared to understand evolutionary aspects of biological processes. RNA consists of the nitrogenous bases adenine (A), uracil (U), cytosine (C), and guanine (G) and can fold into a complex tertiary structure with hair-pin bends that have unpaired bases. Recurring RNA structural motifs have been observed and attributed to biological function. Some of the conformationally recurring motifs include GNRA-like tetraloop, S1, S2, kink turns. Comparative Algorithm to Discover Recurring Elements of Structure (COMPADRES) is an automated approach to identify such recurrent motifs (Wadley and Pyle, 2004). Some of these motifs may contact residues in proteins that are essential for biological function, for example, a pi-turn motif is found on RNA that interacts with ribosomal protein L2.
3. Understanding Proteins: Sequence–Structure–Function Understanding a protein involves understanding its sequence, structure, and function. Primary sequence of a protein can be represented by 20 unique alphabets. Individual properties and standard residue codes for each of these amino acids can be obtained from the following website: http://www.imbjena.de/IMAGE_AA.html#Properties. Studies have shown that amino acids can be exchanged with each other without compromising changes in the structure (Azarya-Sprinzak et al., 1997; Benner et al., 1994; Gonnet et al., 1992; Johnson and Overington, 1993; Jones et al., 1992; Naor et al., 1996). Such exchanges are possible because amino acids share similar physicochemical properties, and changes within similar groups are tolerated (Taylor, 1986). The degree of substitution at a particular residue position depends on the functional role and the environmental location of the residue in the folded form of the protein (Azarya-Sprinzak et al., 1997). Due to this, a number of slightly different sequences may adopt similar structure (divergent evolution) and function. If sequence, structure, and function of a
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set of related proteins are already known then inference rules can be derived. These rules can be applied to classify a new sequence for which no structure or function is known. Such inference rules can be a set of conserved residues like sequence motifs or structural motifs that is present in all the members in a related set of proteins (Falquet et al., 2002; Guruprasad and Shivaprasad, 2000; Hofmann et al., 1999; Hutchinson and Thornton, 1996). The effective means of understanding sequence information coming out of genomic projects will require assigning structure and function. Protein sequences that have evolved from a common parent share similar structure and function. If the parent protein structure is known then one can apply comparative modeling techniques to obtain the geometric information for the unknown protein. Hence, relating protein sequences to their structural parent or to a known fold using computational techniques will be critical to handle biological information effectively.
4. Biological Systems, Signals, and Pathways Many genes are regulated at a given time inside a cell. Regulatory proteins switch these genes on or off based on internal or external cue. A complex network of proteins, small organic molecules, and ions facilitates this regulatory process. For a cell to receive stimuli from the surrounding environment and to devise appropriate responses, signaling pathways are essential. Biological systems have evolved with robust dynamic response to a wide variety of stimuli. Wide positive and negative feedback networks orchestrate such a complex level function. If one considers the multitude of factors which are capable of eliciting cellular responses, it is not surprising that cellular signaling pathways are likely to be extremely complex and diverse. Proteins present in the extracellular environment can come from different sources. Those that are secreted by cells surrounding the “recipient” cell are known as paracrine signals, those that are released by organs distant from the recipient cell are known as endocrine signals, and those released by the cell itself are known as autocrine signals. The proteins in the extracellular milieu can be divided into three broad categories, based upon their effects in the cell: (a) those causing an immediate change in cellular metabolism, (b) those eliciting changes in gene transcription, and (c) those causing fluctuations in electrical conductivity across the plasma membrane. One key aspect of protein binding to the cell surface is specificity. Since the particular molecules binding to the cell surface are intended to elicit specific responses, they must be very selective in the pathways that they initiate. At the same time, it is equally important to
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consider interactions between different cellular pathways, as the cell must respond collectively to a variety of stimuli at any one time. Protein signaling pathways are extremely broad and encompass many different signal transduction pathways. For example the Notch signaling pathway is critical in developmental processes co-ordinated by signal transducer proteins and transcriptional activators, leading to changes at the gene transcription level. The Notch pathway is activated upon contact with neighboring cells expressing Notch ligands. In humans, the ligands that are capable of activating notch are Delta and Serrate. These ligands are membrane bound; therefore, close cell proximity is required for activation of Notch pathways. In some ways, Notch signaling can be considered a “classical” pathway; the binding of Notch ligand to Notch receptor ultimately results in the translocation of the Notch intracellular domain to the nucleus and effects upon gene transcription. Notch ligands are singlepass transmembrane proteins, which contain multiple epidermal growth factors like repeats in the extracellular domain. There are several such signaling pathways that are responsible for widely observed biological processes. A large catalog of such signaling pathways is available at BioCarta pathway listing (http://www.biocarta.com/). Metabolic systems, immune response systems, protein transport, cell cycle and development are some of robust processes that are fundamental to complex biological systems facilitated by macromolecular interactions occurring at different compartments or organelles in the cell. One of the crucial events during evolution that was responsible for the formation of a cell was the development of an outer membrane. With further evolution and selection, the cells of the present day all have a plasma membrane mainly comprised of phospholipids. Classification of cells as prokaryotes and eucaryotes is based on the absence or presence of a functional nucleus that contains DNA. Most cells have a plasma membrane and other organelles such as golgi apparatus, endoplasmic reticulum, nucleus, mitochondria. The first organisms on earth were unicellular such as bacteria and protozoa. So the question that arises is what led to the evolution of multicellular organisms. With our current knowledge of biology, we can explain the origin and importance of cell–cell interactions. Cell–cell interactions are crucial and are part of every aspect of the cell in eukaryotes. These interactions were responsible for the evolution of multicellular organisms. When we define cell–cell interaction it means communication of cells for division, differentiation, reproduction, migration, apoptosis, contact inhibition, etc. There are over 200 types of cells in the human body broadly classified on the basis of the tissue they are present in, namely epithelia, connective tissue, nervous tissue, and muscle. Cooperation among cellular processes is required for the induction of an antibody response in B cells as
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well as for the sensitization of T cells. In addition, the action of activated T cells on target cells is cellular interaction. Macrophages are an essential participant in some of these interactions. The body’s ability to replace dead cells and repair damage is by two distinct processes namely regeneration of injured tissue by parenchymal cells and replacement by connective tissue. The mechanism in both of these processes involves cell growth and differentiation as well as cell–matrix interactions. Several proteins control the timing of the events in the cell cycle, which is tightly regulated to ensure that cells divide only when necessary. The loss of this regulation is the hallmark of cancer, which is also due to loss of control in contact inhibition. Major control switches of the cell cycle are cyclin-dependent kinases. Each cyclin-dependent kinase forms a complex with a particular cyclin, a protein that binds and activates the cyclin-dependent kinase. The kinase part of the complex is an enzyme that adds a phosphate to various proteins required for progression of a cell through the cycle. These added phosphates alter the structure of the protein and can activate or inactivate the protein, depending on its function. There are specific cyclin-dependent kinase/cyclin complexes at the entry points into the G1, S, and M phases of the cell cycle, as well as additional factors that help prepare the cell to enter S phase and M phase. Normal mammalian cells show contact inhibition; that is, they respond to contact with other cells by ceasing cell division. Therefore, cells can divide to fill in a gap, but they stop dividing as soon as there are enough cells to fill the gap. This characteristic is lost in cancer cells, which continue to grow after they touch other cells, causing a large mass of cells to form.
5. Technological Advances and Their Benefits to Biology Technological advances have helped in elucidating the sequence and structure of macromolecules. In 1977, Gilbert and Sanger developed a DNA sequencing method, enzymatic chain termination procedure, and reported the complete genome sequence of bacteriophage fX174 (Sanger et al., 1977). In 1986, Leroy Hood and his co-workers designed the first semi-automated DNA sequencer using Sanger’s chain termination method. Commercial manufacture of DNA sequences by Applied Biosystems® and its later improvements enabled rapid sequencing capacity. Scale-up of automated high-throughput DNA sequencing has enabled rapid accumulation of DNA sequence information. In 1990, human genome and in parallel other genome projects that aimed to sequence the genomic information in organisms completely were planned (Cantor, 1990; Watson, 1990). Owing to the importance of information from scientific projects that determine sequence and structure of biological macromolecules, submission of biological
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sequence and structure information into central databanks has become mandatory. National Center for Biological Information maintains genetic sequence database GenBank. As of August 2005 it contained approximately 47 million sequences (Benson et al., 2005). This excludes many of the ongoing genomic sequences that are yet to be submitted. The discovery of double-helical structure of DNA by Watson and Crick in 1953 using X-ray diffraction patterns led to the understanding of its function and replication mechanism (Watson et al., 1953). This revolutionized biology and basically created the field of molecular biology. Breakthrough in protein X-ray crystallography came with the solution of the phase problem by Perutz and his co-workers in 1954 by applying isomorphous replacement technique (Green et al., 1954). In 1960, John Kendrew and his co-workers solved the first X-ray structure for myoglobin at 6 Å resolution (Kendrew et al., 1958). NMR was later applied to obtain the solution structures of biomolecules (Wagner and Wuthrich, 1979). Protein structures are deposited in the Protein Data Base (PDB) and currently it hosts 32,045 (as of November 2005) protein structures (http://www.rcsb.org) representing thousand unique folds, and 8625 sequences share 50% identity (Berman et al., 2000). Genomic sequencing has resulted in copious amounts of sequence data. DNA sequence alone will not be useful and understanding the complete picture requires annotation of these sequences. Using sampling techniques and extrapolation from the EST experiments, mRNA from known genes, and cross-species gene density comparison, it has been predicted that human genome consists of at least 25,000 genes (Fields et al., 1994; Liang et al., 2000; Roest Crollius et al., 2000; Smaglik, 2000). The information content in genome sequence alone has limited application. Given a DNA sequence gene prediction and gene modeling (Reese et al., 2000) can help in understanding the protein that it codes for in terms of its primary sequence. With the expansion of sequence information it becomes essential to understand the structure, function, interaction, and regulation of proteins in order to understand cellular processes. Microarrays and proteomic technologies enable large-scale study of transcriptome and the proteome, respectively. In this century, technology development has enabled scientists to have necessary tools to study complex biological systems and process. Such studies will shed more light into the complex process underlying every living organism.
6. The Role of Bioinformatics in Big Picture In the above sections, we provided a glimpse of biological organization and complexity. Scientific community is still unraveling many features of life
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using advanced technologies. In recent years, biological science has seen two fields emerge, genomics and proteomics. These fields are rapidly advancing, and bioinformatics provides the tools to analyze and interpret the vast amount of data that is surging. Handling and analyzing biological information is the subject of computational biology and bioinformatics. Computational tasks faced in biology can be broadly divided into selection and classification problems. Classification involves assigning a member to a set or subset that has some defined properties. For example, given a DNA sequence, the classification algorithms attempt to address whether the protein it codes for is a tyrosine kinase. On the other hand, selection algorithms are involved in data mining for example, identifying a DNA repair-related protein or a serine protease from genomic sequence. To deal with these types of problems first of all we need data sets that are accumulated and curated by experts. Next we need algorithms and specific software tools that can provide necessary search, score, and analyze biological information. Chapter 2 defines some of the widely used algorithms. In order to design tools, a bioinformaticist is expected to write effective and deliverable programs. We describe in Chapter 3 scripting languages and database programs that can be used to design simple programs or prototypes that can be delivered over the web. Chapter 4 describes some of the widely available biological information collection in public databases. Chapters 5, 6, and 7 provide details of biological sequence and structure analysis with emphasis on proteins. Chapters 8 and 9 briefly give overview of genomics and proteomics field. With vast amount of information available in journals, biological text mining, and semantic ontologies for organizing this information is gaining importance. Chapter 10 provides text-mining methods. Chapters 11, 12, 13, and 14 describe practical applications and biological problems that can be studied using bioinformatics tools.
7. Exercises 1. Molecular circuits and electronic circuits have feedback controls. Identify biological signaling pathways and electronic circuits that have positive- and negative-feedback. Predict what will happen if such feedback regulations are perturbed. 2. Identify five different signaling pathways and discuss their importance. 3. What are viruses? Name few viruses that infect bacteria. How do viruses replicate? Describe some perturbations caused on a biological system due to virus infection.
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4. Proteins or peptides can function as hormones, enzymes, and transporters. Identify examples for each of them. Compare and contrast glycoproteins and proteoglycans. 5. Describe how proteins are translated and sorted to different organelles in eukaryotes. Compare and contrast this with protein synthesis in prokaryotes.. 6. Orthologs are genes that have evolved across organisms that generally have conserved function. Paralogs are genes that have evolved within organism due to duplication events. Identify orthologs and paralogs in globin gene.
References Azarya-Sprinzak, E., Naor, D., Wolfson, H. J., and Nussinov, R. (1997). Interchanges of spatially neighbouring residues in structurally conserved environments, Protein Eng 10, 1109–22. Benner, S. A., Cohen, M. A., and Gonnet, G. H. (1994). Amino acid substitution during functionally constrained divergent evolution of protein sequences, Protein Eng 7, 1323–32. Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., and Wheeler, D. L. (2005). GenBank, Nucleic Acids Res 33, D34–8. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E. (2000). The Protein Data Bank, Nucleic Acids Res 28, 235–42. Cantor, C. R. (1990). Orchestrating the Human Genome Project, Science 248, 49–51. Falquet, L., Pagni, M., Bucher, P., Hulo, N., Sigrist, C. J., Hofmann, K., and Bairoch, A. (2002). The PROSITE database, its status in 2002, Nucleic Acids Res 30, 235–8. Fields, C., Adams, M. D., White, O., and Venter, J. C. (1994). How many genes in the human genome? Nat Genet 7, 345–6. Gonnet, G. H., Cohen, M. A., and Benner, S. A. (1992). Exhaustive matching of the entire protein sequence database, Science 256, 1443–5. Green, D. W., Ingram, V. M., and Perutz, M. F. (1954). The structure of haemoglobin V. Imidazole-methaemoglobin: a further check of the signs, Proc Roy Soc A225, 287–307. Guruprasad, K., and Shivaprasad, M. (2000). Database of structural motifs in proteins (DSMP), Bioinformatics 16, 373–375. Hofmann, K., Bucher, P., Falquet, L., and Bairoch, A. (1999). The PROSITE database, its status in 1999, Nucleic Acids Res 27, 215–9. Hutchinson, E. G., and Thornton, J. M. (1996). PROMOTIF--a program to identify and analyze structural motifs in proteins, Protein Sci 5, 212–20. Johnson, M. S., and Overington, J. P. (1993). A structural basis for sequence comparisons. An evaluation of scoring methodologies, J Mol Biol 233, 716–38. Jones, D. T., Taylor, W. R., and Thornton, J. M. (1992). The rapid generation of mutation data matrices from protein sequences, Comput Appl Biosci 8, 275–82. Kendrew, J. C., Bodo, G., Dintzis, H. M., Parrish, R. G., Wyckoff, H., and Phillips, D. C. (1958). A three-dimensional model of the myoglobin molecule obtained by x-ray analysis, Nature 181, 662–6. Kitano, H. (2002). Systems biology: a brief overview, Science 295, 1662–4.
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Liang, F., Holt, I., Pertea, G., Karamycheva, S., Salzberg, S. L., and Quackenbush, J. (2000). Gene index analysis of the human genome estimates approximately 120,000 genes, Nat Genet 25, 239–40. Meselson, M., and Stahl, F. W. (1958). The Replication Of Dna In Escherichia Coli, Proc Natl Acad Sci U S A 44, 671–82. Naor, D., Fischer, D., Jernigan, R. L., Wolfson, H. J., and Nussinov, R. (1996). Amino acid pair interchanges at spatially conserved locations, J Mol Biol 256, 924–38. Noble, D. (2002). Modeling the heart--from genes to cells to the whole organ, Science 295, 1678–82. Reese, M. G., Kulp, D., Tammana, H., and Haussler, D. (2000). Genie--gene finding in Drosophila melanogaster, Genome Res 10, 529–38. Roest Crollius, H., Jaillon, O., Bernot, A., Dasilva, C., Bouneau, L., Fischer, C., Fizames, C., Wincker, P., Brottier, P., Quetier, F., et al. (2000). Estimate of human gene number provided by genome-wide analysis using Tetraodon nigroviridis DNA sequence, Nat Genet 25, 235–8. Sanger, F., Nicklen, S., and Coulson, A. R. (1977). DNA sequencing with chain-terminating inhibitors, Proc Natl Acad Sci U S A 74, 5463–7. Smaglik, P. (2000). Researchers take a gamble on the human genome, Nature 405, 264. Taylor, W. R. (1986). The classification of amino acid conservation, J Theor Biol 119, 205–18. Wadley, L. M., and Pyle, A. M. (2004). The identification of novel RNA structural motifs using COMPADRES: an automated approach to structural discovery, Nucleic Acids Res 32, 6650–9. Wagner, G., and Wuthrich, K. (1979). Structural interpretation of the amide proton exchange in the basic pancreatic trypsin inhibitor and related proteins, J Mol Biol 134, 75–94. Watson, J. D. (1990). The human genome project: past, present, and future, Science 248, 44–9. Watson, J. D., and Crick, F. H. (1953). Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid, Nature 171, 737–8.
Chapter 2 Computer Programming Fundamentals and Concepts Deepak N. Kolippakkam1, Pankaj Gupta2, and Venkatarajan S. Mathura 2 1
Harvard University, Boston, USA Roskamp Institute, 2040 Whitfield Avenue, Sarasota, Florida, USA
2
Abstract:
This chapter introduces programming methods and languages that help beginners to learn essentials of computer programming. Two interpreted languages: Perl and PHP are introduced with examples. A popular open source database, MySQL is also included. Additional web-pointers are provided.
Key words:
Perl, PHP, MySQL, RDBMS
1. Purpose These days most modern curricula include computer programming concepts and fundamentals. Not to warrant this assumption about all readers, this chapter is included as a way to pick up techniques for those who haven’t done programming. We introduce two interpreted scripting languages that are widely used in developing bioinformatics applications and MySQLTM database. Scripting languages are powerful yet easy to learn. We outline two scripting languages: Perl and PHP. Pointers are provided to set up a webserver on a Linux platform. After reading and working through all the exercises in this chapter, a reader will be able to roll out their databasedriven bioinformatics web application!
2. Learning Objective • •
Data types, operators, and routines in Perl or PHP Simple scripts to say ‘Hello World!’
V.S. Mathura, P. Kangueane, Bioinformatics: A Concept-Based Introduction, DOI 10.1007/978-0-387-84870-9_2, © Springer Science+Business Media, LLC 2009
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Database design with MySQLTM A simple database-driven web application
3. Perl Programming Perl stands for Practical Extraction and Report Language developed by Larry Wall. It is a powerful language that can be written in a lucid style. The concepts of the language and the syntax are very easy to learn and do not require prior programming experience to start. As a Bioinformatician, you will be expected to be a programmer who can get things done quickly and effectively. Nevertheless, Perl can be used to achieve complex tasks and build an entire application. There are many bioinformatics tools that have been written in Perl and it is a widely used prototyping language to try out your rough sketch or proof of principle. In short, it is a language that will get your job done quickly and effectively. Perl has rich pattern matching and regular expressions that enable text processing effectively, making it attractive for developing bioinformatics applications. It is essential to learn core of the language that includes variables, operators, control structures, functions, and subroutines.
3.1 Variables In Perl, data-structures include scalar, array, and hash. Scalars are represented by a ‘$’ prefix. Scalars can store numeric or string literals. For example, $pH=7.5; $Sequence=”ACCTCCAGAA”; $moltype=’DNA’;
String literals are enclosed in either single or double quotes. Strings literals that have double quotes are interpreted. Numerical literals can be integer or a float. Subtype conversion takes place automatically depending on the context or the operator. For example, $resPos=’5’; $newPos=$resPos+12;
$newPos contains value 17. The string value is automatically converted into a numerical type before addition. Unlike in C or C++, where one needs to specify the variable types and conversions, Perl provides an easy way to define scalar variables. Arrays contain several scalars and can be multidimensional. Arrays are prefixed with the symbol ‘@’. For example, @protein_symbols=(“BCL2”,”APE”,”DAF”);
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Thus, the variable protein_symbols contain a list of three protein names indexed by a number 0, 1, and 2. One can retrieve individual values stored in an array by using the scalar form of the array. print $protein_symbols[0];
will print ‘BCL2’. The index starts from 0; hence, the first array element stored is defined as the scalar with index 0. The last index of a onedimensional array can be obtained using special symbol ‘$#’. For example, print $#protein_symbols;
will print 2. In case of arrays, the index is a numerical key. Perl provides a sophisticated data structure called hash that can index a value based on any key. Hashes are defined using symbol ‘%’ before the variable name. %proteinhash=( “BCL2”=>”B cell lymphoma 2”, “APE”=>”AP endonuclease”, “DAF”=>”Decay accelerating factor” );
Individual values can be retrieved by defining a scalar as: print $proteinhash{‘APE’};
This will print ‘AP endonuclease’. Special functions are available for manipulating both arrays and hashes. In order to obtain all the keys in a hash and store it in an array, one can use the keyword keys. For example, @proteinsymbols=keys %proteinhash;
The above code creates a new array @proteinsymbols using the keys from the hash. Alternatively, if one uses the keyword ‘values’, actual values can be extracted from the hash.
3.2 Operators Standard mathematical operators like addition (+), subtraction (−), multiplication (*), modulus (%), and exponentiation (**) are available. If a + or . operator is used in the context of two strings, then it performs concatenation. Assignment operator = is used to assign a value for variables. If an arithmetic or string concatenation operator is present before the assignment operator, the left side value is operated with the right side value and a new left side value is computed. For example, $a=”hello”; $a.=” world”; print $a;
This will print ‘hello world’. Auto increment or decrement can be performed by prefixing or suffixing of a variable with ++ or --. Logical operators include And (&&), Or (||), Not (!). Comparison operators are equal (== for numeric, eq for string), less than equal (numeric <=, le for string), not equal
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(numeric !=, ne for string), greater than (numeric >, gt for string), less than (numeric <, lt for string), or comparison (numeric <=>, cmp for string).
3.3 Control Structures Perl provides various control structures like if..elsif, unless, while, until, foreach, and for. These commands provide conditional structure, looping, or cycles. There are breaking out commands like next, last, and exit which, if executed, breaks out of the loop or the program. The if structure executes if a condition is satisfied. Multiple if can be combined using elsif. For example, if($molecultype eq “DNA”){ print “It is a DNA\n”; }elsif($molecultype eq “RNA”){ print “It is an RNA\n”; }
If a block of code is required to be executed while a condition is false, then the choice should be unless command. While and until statements can be applied where looping over a condition is required. The for statement can be used for looping some structure for a specified number of times. The structure of a for statement includes a starting value, an exit condition, and an increment operator. For example, to print 1 to 100: for($i=1;$i<101;$i++){ print $i,”\n”; }
If you have an array and would like to loop through the array values, foreach statement should be used. #!/usr/bin/perl #define a hash %proteinhash=( “BCL2”=>”B cell lymphoma 2”, “APE”=>”AP endonuclease”, “DAF”=>”Decay accelerating factor” ); #loop through an array of keys present in the hash foreach(keys %proteinhash){ #push command is used to push a value into an array #{$_} represent current element while looping an array push(@newproteinsymbols,$_); push(@newproteindescp, $proteinhash{$_}); } for($j=0;$j<=$#newproteinsymbols;$j++){ if($newproteinsymbols[$j] eq “BCL2”){ print $newproteinsymbols[$j],”\n”; } }
The above code will print ‘BCL2’.
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3.4 Regular Expressions Perl is a powerful program for regular expression matching. It has UNIX style regular expression and pattern matching. Pattern matching in Perl can be learned best by practising and trying to construct complex expressions. Matching operator evaluates the presence of a pattern or word in a given line and can be used in conditional statements. For example, $present_line=~ m/BIOINFO/;
The above evaluates whether the word ‘BIOINFO’ occurs at any location in a sentence (including partial matches). If you like to find the occurrence in the start then: $present_line=~ m/^BIOINFO/;
or to find a word at the end of a sentence $present_line=~ m/BIOINFORMATICIAN$/;
The above will find the occurrence of a complete word (in this case BIOINFORMATICIAN at the end of the line). If you would like to identify poly-glutamine repeat of exactly 20, one can use special repetitive operators. For example, {n} that follows ‘Q’ in the pattern operation will match repetitive ‘Q’s exactly ‘n’ times. $present_line=”KLQVQQQQQQMMEF”; if($present_line=~ m/Q{6}/){ print “Found a trivial poly Q match”; }
Other repetitive operators include ‘*’ (none or more), ‘.’ (any), {n,m} atleast n occurrences, and not more than m. Escape characters like ‘\n’ for new line, ‘\t’ for tab are available. Additionally, ‘\w’ represents a word, ‘\s’ space character, and ‘\d’ for digit character. Grouping of patterns is possible using () or [] operators. Patterns that are present inside () will be matched and made available in a special variable $1, $2, and so forth depending on the occurrence of () in the pattern. The [] type brackets can be used for ‘or’ operators. For example, [ATE] means residues A, T, or E should occur at a position and (ATE|ALK) means the seqlet ATE or ALK should be present. Let’s write a motif-identifying program that detects phosphorylation site of Caesin II kinase. The pattern of phosphorylated site as documented in PROSITE (PDOC00006) should have an acidic residue (either Asp or Glu), which must be present within three residues from the Cterminal of the phosphate acceptor site. The acceptor site is Ser or Thr. The motif can be represented in regular expression as [ST].{2}[DE]. This expression means that the first position (acceptor site) should have either S or T, followed by exactly any two residues, and finally acidic residues D or E. $seq=”AKKLVFLSDLEMMMMQQQPR”; if($seq= ~m/[ST].{2}[DE]/){ print “Possible Caesin II Kinase site is found”; }
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3.5 File Handling Files in Perl can be opened for reading or writing using the command ‘open’ and a handler keyword. For example, to open a file ‘input.txt’ available in the directory in which the program is being executed: open(INP,”input.txt”);
This command will pass the input as file handle INP which can be read and stored in an array: @data=
; #To close the filehandle close(INP);
and to open a file for writing one should use the symbol ‘>’. For example, open(OUT,”>output.txt”); print OUT “hello world!\n”; close(OUT);
3.6 Subroutines and Functions Subroutines can be written in Perl by passing variables or reference to variables. Each subroutine starts with the word subroutine followed by a unique name. The return command at the end of the subroutine passes back the values to main program. The variables passed into subroutine can be accessed using a special variable @ and local variables that are initialized within subroutine can be defined using ‘my’. ($add_val,$prod_val)=operatemynum(1,7); print $add_val,” “,$prod_val,”\n”; subroutine operatemynum { my ($firstnum, $secondnum, $sumofnum, $prodofnum); $firstnum=@_[0]; $secondnum=@_[1]; $sumofnum=$firstnum+$secondnum; $prodofnum=$firstnum*$secondnum; return ($sumofnum, $prodofnum); }
The subroutine operatemynum returns a sum and product of two numbers. Arrays and hash can be passed as a reference into the subroutine. For example, operateonarrayofnum(\@arrayofnum1,\@arrayofnum2);
To dereference the values one should prefix the variable with ‘$’. For example, to access the value of the first array in firstnums: $value=$$firstnums[0];
Similarly, arrays created inside the subroutine can be passed to main program as a reference.
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So far, a glimpse of Perl language is covered here. Readers who are not familiar with Perl can obtain more information at Comprehensive Perl Archive Network website (http://www.cpan.org). Advanced topics like Perl modules, classes, and other object-oriented subjects are left to the users to learn further.
4. PHP Programming PHP (recursive acronym for PHP: Hypertext Preprocessor) is an open-source scripting language used (but not limited to) web development and generating HTML content (http://www.php.net). The idea of PHP was originally conceived by Robert Lerdorf, but has since been subjected to various changes. The current version of PHP is 5.2.1 (March, 2007). The main feature of PHP is that, it can be embedded into existing HTML documents and can turn a static page into a dynamic data-driven web application. In addition, PHP has a rich array of functions for pattern matching, database connectivity, graphics, image manipulation, XML, etc. PHP can be used in three different ways: • Server-side scripting: Creating dynamic web content including forms, XML documents, graphics, Flash animations, PDF files, etc. • Command-line scripting: Similar to Perl or the Unix shell, PHP can be used to perform system administration tasks, backups, parsing, etc. • Client-side GUI applications: Using the PHP-GTK module, cross platform GUI applications can be created. In the following section, we detail about: 4.1. PHP Language syntax and data types 4.2. Creating Web Interfaces 4.3. Accessing data from an RDBMS – MySQL 4.4. Creating a full fledged data-driven web application using PHP and MySQL The examples in this text would be specific to a web application. Command line scripting examples would be specified otherwise.
4.1 Language Syntax and Data Types Since PHP can be embedded into HTML contents, the PHP interpreter needs to know which of the lines of code are PHP and which are not. In general,
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anything which is enclosed within tags is assumed to be PHP content. Of course, the syntax of the PHP language would be checked at run time. Consider the following piece of code: HTML code begins
”; echo “Why should all programs begin with Hello World
”; // This is a comment /* This is also a comment */ ?> HTML code ends
This code instructs the PHP interpreter to consider the code between the PHP tags as PHP code. The result would be: HTML code begins PHP code Why should all programs begin with Hello World HTML code ends
Now that we have seen our first PHP script in action, let us proceed to discuss some of the data types available in PHP. Akin to all normal programming languages, basic data types include Boolean, integers, floating point numbers, strings, arrays, and objects. PHP also has a data type called ‘resource’ which can hold a reference to an external resource such as a database connection, ftp/ldap operation, etc. To quickly go through these data types, consider the following code snippets Boolean: Variables that hold either ‘True’ or ‘False’ values. Mainly used for testing truth values of certain conditions. $a_flag = True; if ($a_flag) { // do something; }
Integer and floats: Integers are signed/un-signed whole numbers (can be in accord with the decimal, octal, or the hexadecimal number system). Octal numbers need to be preceded with a 0 and hex numbers with a 0X. There are various functions for type conversions to and from integers. $a $b $o $h
= = = =
10; // un-signed integer -23; // signed integer 012; // octal number 0X123; // hexadecimal number
Floats can be used in the same way as integers. Data types need not be declared explicitly (as in C or Java). $complicated_float_number = 1.3; // float example
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Strings: Strings are a sequence of characters (unlimited in size). A variable can be specified as a string in two main ways: single-quoted or doublequoted strings. $string1 = ‘example string’; echo ‘$string1’; // $string2 = “box of”; echo “I got a {$string2} chocolates”; // double quotes allow string parsing with variables echo “I got a {$string2} papers”; echo “where are the \”islets of langerhans\”?\n”; // outputs: where are the “islets of langerhans”? and a line feed. //Note that double quotes within double quotes need to be escaped.
Similar to PHP code being embedded into HTML, the opposite is also possible: HTML code embedded within PHP. This is a special kind of a string specification called ‘heredoc’.
HTML; // some PHP code ?>
Note that ‘print’ can be used in all occasions as ‘echo’ but not vice versa. Arrays: Perhaps the most commonly used data structure in programming is the array. An array holds a list of values, which can be identified by an index (or position). An array can either have just values or keys associated with values. $array1 = array(‘a’,’e’,’i’,o’,’u’); // creates an array of vowels $array1[0] = ‘a’; // creates the same array, although laboriously $array1[1] = ‘e’; $array1[2] = ‘i’; $array1[3] = ‘o’; $array1[4] = ‘u’; print_r($array1); //prints the contents “index =>value” format. $array2 = array(‘Japan’ => ‘Tokyo’, ‘Italy’ => ‘Rome’); // the above array is an associative array, similar to a hash. foreach ($array2 as $country => $capital){ echo “The capital of $country is $capital”, “
”; }// would print each country in the array and its capital. for ($i=0;$i”; } // would print vowels.
Accessing individual array members is possible by specifying the index of that value. Functions are available to sort, merge, reverse, search, etc. on arrays.
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Objects: PHP also supports object-oriented programming. For using the object data type, first a class has to be created. Once that is done, any number of objects can be made using the ‘new’ keyword. class sqs { var $name =’’; function name ($name1 = NULL) { $this->name = $name1; return $this->name; } } // once the class has been defined, objects can be created. $n1 = new sqs; $n1->name(‘ABCD’); echo “Welcome, $n1->name”;
Resources: Communicating to an external resource can be done by using the resource data type. For instance, in our text, we would be providing examples where a database connection is needed and data is transferred back and forth. Depending on what kind of a database is being used, the connection method and the parameters to be passed would differ. For example, $db=mysql_connect("localhost","username","password"); mysql_select_db("sampledb",$db);
This code creates a connection to a MySQL database using the given user name and password. It specifically establishes a connection to the database named ‘sampledb’. On successful connection, the user would be able to access individual tables, make joins, etc. Hence, $result = mysql_query(“select PROT_NAME from pdbase where PROT_ID = ‘23228’”,$db);
can be executed. We would discuss about database connectivity in more detail in 4.3.
4.2 Creating Web Interfaces One of the main uses of PHP is its ability to create dynamic web content. PHP can be used to access form values, upload files, send cookies, set/unset sessions, etc. Consider the following simple HTML form: Simple form1 Enter Information
